Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Quality Level Classification of ECG Measured using Non-Constraint Approach

무구속적 방법으로 측정된 심전도의 신뢰도 판별

  • Kim, Y.J. (Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University) ;
  • Heo, J. (Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University) ;
  • Park, K.S. (Department of Biomedical Engineering, Seoul National University College of Medicine) ;
  • Kim, S. (Department of Biomedical Engineering, Seoul National University College of Medicine)
  • 김윤재 (서울대학교 대학원 협동과정 바이오엔지니어링 전공) ;
  • 허정 (서울대학교 대학원 협동과정 바이오엔지니어링 전공) ;
  • 박광석 (서울대학교 의과대학 의학과 의공학교실) ;
  • 김성완 (서울대학교 의과대학 의학과 의공학교실)
  • Received : 2016.08.05
  • Accepted : 2016.10.20
  • Published : 2016.10.31
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Abstract

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.

Keywords

References

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